2 citations · 3 across the 6 of their papers we have counts for
6 papers
SpSYRK: Half the Work in Distributed Sparse Matrix Multiplication
Thomas McFarland, Julian Bellavita, Giulia Guidi
The symmetric rank- update (SYRK), , computes the dot product of each pair of rows of , producing the Gram matrix . Its sparse variant underpins similarity se…
Communication-Avoiding SpGEMM via Trident Partitioning on Hierarchical GPU Interconnects
Julian Bellavita, Lorenzo Pichetti, Thomas Pasquali +2
The multiplication of two sparse matrices, known as SpGEMM, is a key kernel in scientific computing and large-scale data analytics, underpinning graph algorithms, machine learning,…
Communication-Avoiding Linear Algebraic Kernel K-Means on GPUs
Julian Bellavita, Matthew Rubino, Nakul Iyer +4
Clustering is an important tool in data analysis, with K-means being popular for its simplicity and versatility. However, it cannot handle non-linearly separable clusters. Kernel K…
Parallel GPU-Enabled Algorithms for SpGEMM on Arbitrary Semirings with Hybrid Communication
Thomas McFarland, Julian Bellavita, Giulia Guidi
Sparse General Matrix Multiply (SpGEMM) is key for various High-Performance Computing (HPC) applications such as genomics and graph analytics. Using the semiring abstraction, many…
Popcorn: Accelerating Kernel K-means on GPUs through Sparse Linear Algebra
Julian Bellavita, Thomas Pasquali, Laura Del Rio Martin +2
K-means is a popular clustering algorithm with significant applications in numerous scientific and engineering areas. One drawback of K-means is its inability to identify non-linea…
Tackling the Matrix Multiplication Micro-kernel Generation with Exo
Adrián Castelló, Julian Bellavita, Grace Dinh +2
The optimization of the matrix multiplication (or GEMM) has been a need during the last decades. This operation is considered the flagship of current linear algebra libraries such…